关键词: 交易系统, 银行, 预测, 告警, Spark

Abstract: Banks generally have a wide variety of co-existing transaction systems, when these decentralized transaction systems malfunction, it is difficult for the maintenance support engineer to locate the cause of malfunction from the massive logs. For the problems above, Spark Streaming, Spark ML, ELK and other technologies are used to design and implement the monitoring and alarming platform oriented to banking business, which is based on decision tree regression model. The platform can monitor the transaction volume of the transaction system in real time and predict the transaction volume of different transaction systems in different periods. The predicted values are dynamic thresholds of the transaction volume. The platform can alarm the abnormal system according to the threshold in real time. Operation results of real environment show that the platform satisfies the demand of maintenance of the bank transaction systems well.

Key words: transaction system, bank, predict, alarm, Spark